Whale Optimization Algorithm

The whale optimization algorithm is a population-based metaheuristic optimization algorithm that models the bubble-net hunting strategy of humpback whales. Each candidate solution in the population represents a whale, and the algorithm alternates between an exploitation phase, in which whales encircle and spiral in on the best solution found so far, mimicking a pod of whales tightening a ring of bubbles around a school of prey, and an exploration phase, in which a whale instead moves toward a randomly chosen member of the population rather than the current best, keeping the search from converging too quickly onto a single region; a probability parameter decides at each step which of the two movement strategies a given whale follows. The algorithm was introduced in a 2016 research paper as part of the broader family of nature-inspired swarm metaheuristics that also includes particle swarm optimization and the grey wolf optimizer, and it has since been applied to engineering design and other numerical optimization problems.